Research Articles
Transformer-Based Molecular Generators: A 2025 Performance Comparison and Application Guide
This article provides a comprehensive performance comparison of transformer-based generative models for molecular design, a key technology in modern drug discovery.
Benchmarking Generative AI for Molecular Design: Models, Metrics, and Real-World Impact
This article provides a comprehensive analysis of the current state and critical challenges in benchmarking generative artificial intelligence models for molecular design.
Taming Polymorphs: How Generative AI Models Are Revolutionizing Material Design and Drug Development
This article explores the critical challenge of handling polymorph representation within generative AI models for material science.
Stabilizing GANs: Overcoming Training Instability for Robust Biomedical AI
This article provides a comprehensive guide for researchers and drug development professionals on overcoming the pervasive challenge of training instability in Generative Adversarial Networks (GANs).
Overcoming Mode Collapse in Generative AI for Materials Discovery: Strategies for Robust Molecular Design
This article provides a comprehensive analysis of mode collapse, a critical failure in generative AI models where output diversity severely degrades, hindering the discovery of novel materials and drugs.
Overcoming Data Scarcity in Generative AI for Materials Science: Strategies for Drug Discovery and Biomedical Innovation
This article addresses the critical challenge of data scarcity that constrains the development of robust generative AI models in materials science and drug discovery.
3D Molecular Generation: From Spatial Representations to Drug Discovery Applications
The integration of three-dimensional molecular representations into generative artificial intelligence is revolutionizing computational drug discovery.
Foundation Models for Materials Synthesis: Accelerating Discovery from Prediction to Production
This article explores the transformative role of foundation models in materials synthesis planning, a critical bottleneck in materials science and drug development.
Conditional Generation for Targeted Material Properties: AI-Driven Design in Drug Discovery and Beyond
This article explores the transformative role of conditional generative models in designing materials and molecules with precisely targeted properties.
Multi-Objective Optimization in Molecular Generation: AI-Driven Strategies for Balanced Drug Design
The design of novel drug candidates necessitates balancing multiple, often competing, molecular properties such as potency, selectivity, metabolic stability, and low toxicity.